Search Results for author:
Found 12 papers, 9 papers with code
Unsegment Anything by Simulating Deformation
Foundation segmentation models, while powerful, pose a significant risk: they enable users to effortlessly extract any objects from any digital content with a single click, potentially leading to copyright infringement or malicious misuse.
Unsupervised Template-assisted Point Cloud Shape Correspondence Network
Unsupervised point cloud shape correspondence aims to establish point-wise correspondences between source and target point clouds.
BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation
To generate higher quality pseudo-labels and achieve more precise weakly supervised 3DIS results, we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (BSNet), which devises a novel pseudo-labeler called Simulation-assisted Transformer.
SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence
The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts.
Ranked #2 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
Sliding at first order: Higher-order momentum distributions for discontinuous image registration
In this paper, we propose a new approach to deformable image registration that captures sliding motions.
Query Refinement Transformer for 3D Instance Segmentation
Additionally, we design an affiliated transformer decoder that suppresses the interference of noise background queries and helps the foreground queries focus on instance discriminative parts to predict final segmentation results.
Ranked #3 on
3D Instance Segmentation
on ScanNet(v2)
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule Diagnosis
Recently, attempts have been made to reduce annotation requirements in feature-based self-explanatory models for lung nodule diagnosis.
Reducing Annotation Need in Self-Explanatory Models for Lung Nodule Diagnosis
Visualisation of the learned space further indicates that the correlation between the clustering of malignancy and nodule attributes coincides with clinical knowledge.
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers
Showing how vision Transformers are at the risk of privacy leakage via gradients, we urge the significance of designing privacy-safer Transformer models and defending schemes.
Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study
We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration.
CoMIR: Contrastive Multimodal Image Representation for Registration
We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations).
A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification.
